Overview

Dataset statistics

Number of variables29
Number of observations4300
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.1 MiB
Average record size in memory270.7 B

Variable types

Numeric11
Boolean2
Categorical16

Alerts

EmployeeCount has constant value ""Constant
Over18 has constant value ""Constant
StandardHours has constant value ""Constant
Age is highly overall correlated with TotalWorkingYearsHigh correlation
Department is highly overall correlated with EducationFieldHigh correlation
EducationField is highly overall correlated with DepartmentHigh correlation
PercentSalaryHike is highly overall correlated with PerformanceRatingHigh correlation
PerformanceRating is highly overall correlated with PercentSalaryHikeHigh correlation
TotalWorkingYears is highly overall correlated with Age and 1 other fieldsHigh correlation
YearsAtCompany is highly overall correlated with TotalWorkingYears and 2 other fieldsHigh correlation
YearsSinceLastPromotion is highly overall correlated with YearsAtCompanyHigh correlation
YearsWithCurrManager is highly overall correlated with YearsAtCompanyHigh correlation
EmployeeID is uniformly distributedUniform
EmployeeID has unique valuesUnique
NumCompaniesWorked has 570 (13.3%) zerosZeros
TrainingTimesLastYear has 161 (3.7%) zerosZeros
YearsAtCompany has 126 (2.9%) zerosZeros
YearsSinceLastPromotion has 1697 (39.5%) zerosZeros
YearsWithCurrManager has 760 (17.7%) zerosZeros

Reproduction

Analysis started2023-12-05 01:36:05.013432
Analysis finished2023-12-05 01:37:06.553263
Duration1 minute and 1.54 second
Software versionydata-profiling vv4.6.2
Download configurationconfig.json

Variables

EmployeeID
Real number (ℝ)

UNIFORM  UNIQUE 

Distinct4300
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2211.6951
Minimum1
Maximum4409
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size196.2 KiB
2023-12-05T01:37:06.760036image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile225.95
Q11110.75
median2215.5
Q33314.25
95-th percentile4188.05
Maximum4409
Range4408
Interquartile range (IQR)2203.5

Descriptive statistics

Standard deviation1272.1177
Coefficient of variation (CV)0.57517769
Kurtosis-1.2010098
Mean2211.6951
Median Absolute Deviation (MAD)1102
Skewness-0.0063026598
Sum9510289
Variance1618283.4
MonotonicityStrictly increasing
2023-12-05T01:37:07.040596image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
< 0.1%
2955 1
 
< 0.1%
2941 1
 
< 0.1%
2942 1
 
< 0.1%
2943 1
 
< 0.1%
2944 1
 
< 0.1%
2945 1
 
< 0.1%
2946 1
 
< 0.1%
2947 1
 
< 0.1%
2948 1
 
< 0.1%
Other values (4290) 4290
99.8%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
ValueCountFrequency (%)
4409 1
< 0.1%
4408 1
< 0.1%
4407 1
< 0.1%
4406 1
< 0.1%
4405 1
< 0.1%
4404 1
< 0.1%
4403 1
< 0.1%
4402 1
< 0.1%
4401 1
< 0.1%
4400 1
< 0.1%

Age
Real number (ℝ)

HIGH CORRELATION 

Distinct43
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.926977
Minimum18
Maximum60
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size196.2 KiB
2023-12-05T01:37:07.321270image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile24
Q130
median36
Q343
95-th percentile54
Maximum60
Range42
Interquartile range (IQR)13

Descriptive statistics

Standard deviation9.1465171
Coefficient of variation (CV)0.24769201
Kurtosis-0.40679122
Mean36.926977
Median Absolute Deviation (MAD)6
Skewness0.41556547
Sum158786
Variance83.658774
MonotonicityNot monotonic
2023-12-05T01:37:07.590657image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
35 229
 
5.3%
34 226
 
5.3%
36 200
 
4.7%
31 199
 
4.6%
29 198
 
4.6%
32 179
 
4.2%
30 176
 
4.1%
33 173
 
4.0%
38 171
 
4.0%
40 163
 
3.8%
Other values (33) 2386
55.5%
ValueCountFrequency (%)
18 22
 
0.5%
19 27
 
0.6%
20 33
 
0.8%
21 38
 
0.9%
22 47
 
1.1%
23 42
 
1.0%
24 77
1.8%
25 77
1.8%
26 111
2.6%
27 141
3.3%
ValueCountFrequency (%)
60 15
 
0.3%
59 30
0.7%
58 42
1.0%
57 12
 
0.3%
56 40
0.9%
55 65
1.5%
54 53
1.2%
53 55
1.3%
52 51
1.2%
51 55
1.3%

Attrition
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size166.8 KiB
False
3605 
True
695 
ValueCountFrequency (%)
False 3605
83.8%
True 695
 
16.2%
2023-12-05T01:37:07.902575image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

BusinessTravel
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size196.2 KiB
Travel_Rarely
3051 
Travel_Frequently
809 
Non-Travel
440 

Length

Max length17
Median length13
Mean length13.445581
Min length10

Characters and Unicode

Total characters57816
Distinct characters17
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTravel_Rarely
2nd rowTravel_Frequently
3rd rowTravel_Frequently
4th rowNon-Travel
5th rowTravel_Rarely

Common Values

ValueCountFrequency (%)
Travel_Rarely 3051
71.0%
Travel_Frequently 809
 
18.8%
Non-Travel 440
 
10.2%

Length

2023-12-05T01:37:08.138398image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-05T01:37:08.436877image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
travel_rarely 3051
71.0%
travel_frequently 809
 
18.8%
non-travel 440
 
10.2%

Most occurring characters

ValueCountFrequency (%)
e 8969
15.5%
r 8160
14.1%
l 8160
14.1%
a 7351
12.7%
T 4300
7.4%
v 4300
7.4%
y 3860
6.7%
_ 3860
6.7%
R 3051
 
5.3%
n 1249
 
2.2%
Other values (7) 4556
7.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 44916
77.7%
Uppercase Letter 8600
 
14.9%
Connector Punctuation 3860
 
6.7%
Dash Punctuation 440
 
0.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 8969
20.0%
r 8160
18.2%
l 8160
18.2%
a 7351
16.4%
v 4300
9.6%
y 3860
8.6%
n 1249
 
2.8%
q 809
 
1.8%
u 809
 
1.8%
t 809
 
1.8%
Uppercase Letter
ValueCountFrequency (%)
T 4300
50.0%
R 3051
35.5%
F 809
 
9.4%
N 440
 
5.1%
Connector Punctuation
ValueCountFrequency (%)
_ 3860
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 440
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 53516
92.6%
Common 4300
 
7.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 8969
16.8%
r 8160
15.2%
l 8160
15.2%
a 7351
13.7%
T 4300
8.0%
v 4300
8.0%
y 3860
7.2%
R 3051
 
5.7%
n 1249
 
2.3%
F 809
 
1.5%
Other values (5) 3307
 
6.2%
Common
ValueCountFrequency (%)
_ 3860
89.8%
- 440
 
10.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 57816
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 8969
15.5%
r 8160
14.1%
l 8160
14.1%
a 7351
12.7%
T 4300
7.4%
v 4300
7.4%
y 3860
6.7%
_ 3860
6.7%
R 3051
 
5.3%
n 1249
 
2.2%
Other values (7) 4556
7.9%

Department
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size196.2 KiB
Research & Development
2807 
Sales
1307 
Human Resources
 
186

Length

Max length22
Median length22
Mean length16.53
Min length5

Characters and Unicode

Total characters71079
Distinct characters20
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSales
2nd rowResearch & Development
3rd rowResearch & Development
4th rowResearch & Development
5th rowResearch & Development

Common Values

ValueCountFrequency (%)
Research & Development 2807
65.3%
Sales 1307
30.4%
Human Resources 186
 
4.3%

Length

2023-12-05T01:37:08.701775image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-05T01:37:08.990291image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
research 2807
27.8%
2807
27.8%
development 2807
27.8%
sales 1307
12.9%
human 186
 
1.8%
resources 186
 
1.8%

Most occurring characters

ValueCountFrequency (%)
e 15714
22.1%
5800
 
8.2%
s 4486
 
6.3%
a 4300
 
6.0%
l 4114
 
5.8%
R 2993
 
4.2%
r 2993
 
4.2%
c 2993
 
4.2%
n 2993
 
4.2%
m 2993
 
4.2%
Other values (10) 21700
30.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 55179
77.6%
Uppercase Letter 7293
 
10.3%
Space Separator 5800
 
8.2%
Other Punctuation 2807
 
3.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 15714
28.5%
s 4486
 
8.1%
a 4300
 
7.8%
l 4114
 
7.5%
r 2993
 
5.4%
c 2993
 
5.4%
n 2993
 
5.4%
m 2993
 
5.4%
o 2993
 
5.4%
p 2807
 
5.1%
Other values (4) 8793
15.9%
Uppercase Letter
ValueCountFrequency (%)
R 2993
41.0%
D 2807
38.5%
S 1307
17.9%
H 186
 
2.6%
Space Separator
ValueCountFrequency (%)
5800
100.0%
Other Punctuation
ValueCountFrequency (%)
& 2807
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 62472
87.9%
Common 8607
 
12.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 15714
25.2%
s 4486
 
7.2%
a 4300
 
6.9%
l 4114
 
6.6%
R 2993
 
4.8%
r 2993
 
4.8%
c 2993
 
4.8%
n 2993
 
4.8%
m 2993
 
4.8%
o 2993
 
4.8%
Other values (8) 15900
25.5%
Common
ValueCountFrequency (%)
5800
67.4%
& 2807
32.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 71079
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 15714
22.1%
5800
 
8.2%
s 4486
 
6.3%
a 4300
 
6.0%
l 4114
 
5.8%
R 2993
 
4.2%
r 2993
 
4.2%
c 2993
 
4.2%
n 2993
 
4.2%
m 2993
 
4.2%
Other values (10) 21700
30.5%

DistanceFromHome
Real number (ℝ)

Distinct29
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.197907
Minimum1
Maximum29
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size196.2 KiB
2023-12-05T01:37:09.225286image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median7
Q314
95-th percentile26
Maximum29
Range28
Interquartile range (IQR)12

Descriptive statistics

Standard deviation8.0970587
Coefficient of variation (CV)0.88031535
Kurtosis-0.22129283
Mean9.197907
Median Absolute Deviation (MAD)5
Skewness0.95626371
Sum39551
Variance65.562359
MonotonicityNot monotonic
2023-12-05T01:37:09.465418image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
2 614
14.3%
1 612
14.2%
10 256
 
6.0%
9 251
 
5.8%
7 246
 
5.7%
3 243
 
5.7%
8 235
 
5.5%
5 186
 
4.3%
4 185
 
4.3%
6 172
 
4.0%
Other values (19) 1300
30.2%
ValueCountFrequency (%)
1 612
14.2%
2 614
14.3%
3 243
 
5.7%
4 185
 
4.3%
5 186
 
4.3%
6 172
 
4.0%
7 246
5.7%
8 235
 
5.5%
9 251
5.8%
10 256
6.0%
ValueCountFrequency (%)
29 80
1.9%
28 66
1.5%
27 36
0.8%
26 74
1.7%
25 73
1.7%
24 80
1.9%
23 76
1.8%
22 55
1.3%
21 53
1.2%
20 73
1.7%

Education
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size196.2 KiB
3
1670 
4
1168 
2
823 
1
499 
5
 
140

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4300
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row1
3rd row4
4th row5
5th row1

Common Values

ValueCountFrequency (%)
3 1670
38.8%
4 1168
27.2%
2 823
19.1%
1 499
 
11.6%
5 140
 
3.3%

Length

2023-12-05T01:37:09.730319image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-05T01:37:10.004418image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
3 1670
38.8%
4 1168
27.2%
2 823
19.1%
1 499
 
11.6%
5 140
 
3.3%

Most occurring characters

ValueCountFrequency (%)
3 1670
38.8%
4 1168
27.2%
2 823
19.1%
1 499
 
11.6%
5 140
 
3.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4300
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 1670
38.8%
4 1168
27.2%
2 823
19.1%
1 499
 
11.6%
5 140
 
3.3%

Most occurring scripts

ValueCountFrequency (%)
Common 4300
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 1670
38.8%
4 1168
27.2%
2 823
19.1%
1 499
 
11.6%
5 140
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4300
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 1670
38.8%
4 1168
27.2%
2 823
19.1%
1 499
 
11.6%
5 140
 
3.3%

EducationField
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size196.2 KiB
Life Sciences
1766 
Medical
1364 
Marketing
469 
Technical Degree
384 
Other
237 

Length

Max length16
Median length15
Mean length10.524651
Min length5

Characters and Unicode

Total characters45256
Distinct characters26
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLife Sciences
2nd rowLife Sciences
3rd rowOther
4th rowLife Sciences
5th rowMedical

Common Values

ValueCountFrequency (%)
Life Sciences 1766
41.1%
Medical 1364
31.7%
Marketing 469
 
10.9%
Technical Degree 384
 
8.9%
Other 237
 
5.5%
Human Resources 80
 
1.9%

Length

2023-12-05T01:37:10.260706image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-05T01:37:10.568597image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
life 1766
27.0%
sciences 1766
27.0%
medical 1364
20.9%
marketing 469
 
7.2%
technical 384
 
5.9%
degree 384
 
5.9%
other 237
 
3.6%
human 80
 
1.2%
resources 80
 
1.2%

Most occurring characters

ValueCountFrequency (%)
e 9064
20.0%
i 5749
12.7%
c 5744
12.7%
n 2699
 
6.0%
a 2297
 
5.1%
2230
 
4.9%
s 1926
 
4.3%
M 1833
 
4.1%
L 1766
 
3.9%
f 1766
 
3.9%
Other values (16) 10182
22.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 36496
80.6%
Uppercase Letter 6530
 
14.4%
Space Separator 2230
 
4.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 9064
24.8%
i 5749
15.8%
c 5744
15.7%
n 2699
 
7.4%
a 2297
 
6.3%
s 1926
 
5.3%
f 1766
 
4.8%
l 1748
 
4.8%
d 1364
 
3.7%
r 1170
 
3.2%
Other values (7) 2969
 
8.1%
Uppercase Letter
ValueCountFrequency (%)
M 1833
28.1%
L 1766
27.0%
S 1766
27.0%
T 384
 
5.9%
D 384
 
5.9%
O 237
 
3.6%
H 80
 
1.2%
R 80
 
1.2%
Space Separator
ValueCountFrequency (%)
2230
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 43026
95.1%
Common 2230
 
4.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 9064
21.1%
i 5749
13.4%
c 5744
13.4%
n 2699
 
6.3%
a 2297
 
5.3%
s 1926
 
4.5%
M 1833
 
4.3%
L 1766
 
4.1%
f 1766
 
4.1%
S 1766
 
4.1%
Other values (15) 8416
19.6%
Common
ValueCountFrequency (%)
2230
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 45256
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 9064
20.0%
i 5749
12.7%
c 5744
12.7%
n 2699
 
6.0%
a 2297
 
5.1%
2230
 
4.9%
s 1926
 
4.3%
M 1833
 
4.1%
L 1766
 
3.9%
f 1766
 
3.9%
Other values (16) 10182
22.5%

EmployeeCount
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size196.2 KiB
1
4300 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4300
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 4300
100.0%

Length

2023-12-05T01:37:10.836246image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-05T01:37:11.077701image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 4300
100.0%

Most occurring characters

ValueCountFrequency (%)
1 4300
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4300
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 4300
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4300
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 4300
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4300
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 4300
100.0%

Gender
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size196.2 KiB
Male
2571 
Female
1729 

Length

Max length6
Median length4
Mean length4.804186
Min length4

Characters and Unicode

Total characters20658
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFemale
2nd rowFemale
3rd rowMale
4th rowMale
5th rowMale

Common Values

ValueCountFrequency (%)
Male 2571
59.8%
Female 1729
40.2%

Length

2023-12-05T01:37:11.291019image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-05T01:37:11.574638image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
male 2571
59.8%
female 1729
40.2%

Most occurring characters

ValueCountFrequency (%)
e 6029
29.2%
a 4300
20.8%
l 4300
20.8%
M 2571
12.4%
F 1729
 
8.4%
m 1729
 
8.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 16358
79.2%
Uppercase Letter 4300
 
20.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 6029
36.9%
a 4300
26.3%
l 4300
26.3%
m 1729
 
10.6%
Uppercase Letter
ValueCountFrequency (%)
M 2571
59.8%
F 1729
40.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 20658
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 6029
29.2%
a 4300
20.8%
l 4300
20.8%
M 2571
12.4%
F 1729
 
8.4%
m 1729
 
8.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20658
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 6029
29.2%
a 4300
20.8%
l 4300
20.8%
M 2571
12.4%
F 1729
 
8.4%
m 1729
 
8.4%

JobLevel
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size196.2 KiB
1
1582 
2
1563 
3
641 
4
313 
5
201 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4300
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row4
4th row3
5th row1

Common Values

ValueCountFrequency (%)
1 1582
36.8%
2 1563
36.3%
3 641
14.9%
4 313
 
7.3%
5 201
 
4.7%

Length

2023-12-05T01:37:11.787156image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-05T01:37:12.053813image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 1582
36.8%
2 1563
36.3%
3 641
14.9%
4 313
 
7.3%
5 201
 
4.7%

Most occurring characters

ValueCountFrequency (%)
1 1582
36.8%
2 1563
36.3%
3 641
14.9%
4 313
 
7.3%
5 201
 
4.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4300
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1582
36.8%
2 1563
36.3%
3 641
14.9%
4 313
 
7.3%
5 201
 
4.7%

Most occurring scripts

ValueCountFrequency (%)
Common 4300
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1582
36.8%
2 1563
36.3%
3 641
14.9%
4 313
 
7.3%
5 201
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4300
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1582
36.8%
2 1563
36.3%
3 641
14.9%
4 313
 
7.3%
5 201
 
4.7%

JobRole
Categorical

Distinct9
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size196.2 KiB
Sales Executive
956 
Research Scientist
859 
Laboratory Technician
757 
Manufacturing Director
422 
Healthcare Representative
377 
Other values (4)
929 

Length

Max length25
Median length21
Mean length18.052558
Min length7

Characters and Unicode

Total characters77626
Distinct characters29
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHealthcare Representative
2nd rowResearch Scientist
3rd rowSales Executive
4th rowHuman Resources
5th rowSales Executive

Common Values

ValueCountFrequency (%)
Sales Executive 956
22.2%
Research Scientist 859
20.0%
Laboratory Technician 757
17.6%
Manufacturing Director 422
9.8%
Healthcare Representative 377
 
8.8%
Manager 299
 
7.0%
Sales Representative 241
 
5.6%
Research Director 235
 
5.5%
Human Resources 154
 
3.6%

Length

2023-12-05T01:37:12.315584image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-05T01:37:12.648494image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
sales 1197
14.4%
research 1094
13.2%
executive 956
11.5%
scientist 859
10.3%
laboratory 757
9.1%
technician 757
9.1%
director 657
7.9%
representative 618
7.4%
manufacturing 422
 
5.1%
healthcare 377
 
4.5%
Other values (3) 607
7.3%

Most occurring characters

ValueCountFrequency (%)
e 11403
14.7%
a 7530
 
9.7%
t 6123
 
7.9%
c 6033
 
7.8%
i 5885
 
7.6%
r 5792
 
7.5%
n 4288
 
5.5%
s 4076
 
5.3%
4001
 
5.2%
o 2325
 
3.0%
Other values (19) 20170
26.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 65324
84.2%
Uppercase Letter 8301
 
10.7%
Space Separator 4001
 
5.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 11403
17.5%
a 7530
11.5%
t 6123
9.4%
c 6033
9.2%
i 5885
9.0%
r 5792
8.9%
n 4288
 
6.6%
s 4076
 
6.2%
o 2325
 
3.6%
h 2228
 
3.4%
Other values (10) 9641
14.8%
Uppercase Letter
ValueCountFrequency (%)
S 2056
24.8%
R 1866
22.5%
E 956
11.5%
L 757
 
9.1%
T 757
 
9.1%
M 721
 
8.7%
D 657
 
7.9%
H 531
 
6.4%
Space Separator
ValueCountFrequency (%)
4001
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 73625
94.8%
Common 4001
 
5.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 11403
15.5%
a 7530
10.2%
t 6123
 
8.3%
c 6033
 
8.2%
i 5885
 
8.0%
r 5792
 
7.9%
n 4288
 
5.8%
s 4076
 
5.5%
o 2325
 
3.2%
h 2228
 
3.0%
Other values (18) 17942
24.4%
Common
ValueCountFrequency (%)
4001
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 77626
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 11403
14.7%
a 7530
 
9.7%
t 6123
 
7.9%
c 6033
 
7.8%
i 5885
 
7.6%
r 5792
 
7.5%
n 4288
 
5.5%
s 4076
 
5.3%
4001
 
5.2%
o 2325
 
3.0%
Other values (19) 20170
26.0%

MaritalStatus
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size196.2 KiB
Married
1969 
Single
1382 
Divorced
949 

Length

Max length8
Median length7
Mean length6.8993023
Min length6

Characters and Unicode

Total characters29667
Distinct characters14
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMarried
2nd rowSingle
3rd rowMarried
4th rowMarried
5th rowSingle

Common Values

ValueCountFrequency (%)
Married 1969
45.8%
Single 1382
32.1%
Divorced 949
22.1%

Length

2023-12-05T01:37:12.979233image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-05T01:37:13.267824image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
married 1969
45.8%
single 1382
32.1%
divorced 949
22.1%

Most occurring characters

ValueCountFrequency (%)
r 4887
16.5%
i 4300
14.5%
e 4300
14.5%
d 2918
9.8%
M 1969
6.6%
a 1969
6.6%
S 1382
 
4.7%
n 1382
 
4.7%
g 1382
 
4.7%
l 1382
 
4.7%
Other values (4) 3796
12.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 25367
85.5%
Uppercase Letter 4300
 
14.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 4887
19.3%
i 4300
17.0%
e 4300
17.0%
d 2918
11.5%
a 1969
7.8%
n 1382
 
5.4%
g 1382
 
5.4%
l 1382
 
5.4%
v 949
 
3.7%
o 949
 
3.7%
Uppercase Letter
ValueCountFrequency (%)
M 1969
45.8%
S 1382
32.1%
D 949
22.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 29667
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 4887
16.5%
i 4300
14.5%
e 4300
14.5%
d 2918
9.8%
M 1969
6.6%
a 1969
6.6%
S 1382
 
4.7%
n 1382
 
4.7%
g 1382
 
4.7%
l 1382
 
4.7%
Other values (4) 3796
12.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 29667
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 4887
16.5%
i 4300
14.5%
e 4300
14.5%
d 2918
9.8%
M 1969
6.6%
a 1969
6.6%
S 1382
 
4.7%
n 1382
 
4.7%
g 1382
 
4.7%
l 1382
 
4.7%
Other values (4) 3796
12.8%

MonthlyIncome
Real number (ℝ)

Distinct1238
Distinct (%)28.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean63635.386
Minimum10090
Maximum165616.25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size196.2 KiB
2023-12-05T01:37:13.522842image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum10090
5-th percentile20960
Q129260
median49360
Q383802.5
95-th percentile165616.25
Maximum165616.25
Range155526.25
Interquartile range (IQR)54542.5

Descriptive statistics

Standard deviation43465.668
Coefficient of variation (CV)0.68304242
Kurtosis0.29154985
Mean63635.386
Median Absolute Deviation (MAD)22080
Skewness1.1642845
Sum2.7363216 × 108
Variance1.8892643 × 109
MonotonicityNot monotonic
2023-12-05T01:37:13.829142image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
165616.25 331
 
7.7%
23420 12
 
0.3%
24040 9
 
0.2%
23800 9
 
0.2%
34520 9
 
0.2%
27410 9
 
0.2%
24510 9
 
0.2%
63470 9
 
0.2%
25590 9
 
0.2%
26100 9
 
0.2%
Other values (1228) 3885
90.3%
ValueCountFrequency (%)
10090 3
0.1%
10510 3
0.1%
10520 3
0.1%
10810 2
< 0.1%
10910 3
0.1%
11020 3
0.1%
11180 3
0.1%
11290 3
0.1%
12000 3
0.1%
12230 3
0.1%
ValueCountFrequency (%)
165616.25 331
7.7%
165550 3
 
0.1%
164370 3
 
0.1%
164220 3
 
0.1%
164130 3
 
0.1%
163280 3
 
0.1%
163070 3
 
0.1%
162910 3
 
0.1%
161840 3
 
0.1%
161240 3
 
0.1%

NumCompaniesWorked
Real number (ℝ)

ZEROS 

Distinct10
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.69
Minimum0
Maximum9
Zeros570
Zeros (%)13.3%
Negative0
Negative (%)0.0%
Memory size196.2 KiB
2023-12-05T01:37:14.095707image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q34
95-th percentile8
Maximum9
Range9
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.4957638
Coefficient of variation (CV)0.92779323
Kurtosis0.024837301
Mean2.69
Median Absolute Deviation (MAD)1
Skewness1.0332713
Sum11567
Variance6.2288369
MonotonicityNot monotonic
2023-12-05T01:37:14.778024image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1 1535
35.7%
0 570
 
13.3%
3 466
 
10.8%
2 427
 
9.9%
4 404
 
9.4%
7 217
 
5.0%
6 201
 
4.7%
5 185
 
4.3%
9 154
 
3.6%
8 141
 
3.3%
ValueCountFrequency (%)
0 570
 
13.3%
1 1535
35.7%
2 427
 
9.9%
3 466
 
10.8%
4 404
 
9.4%
5 185
 
4.3%
6 201
 
4.7%
7 217
 
5.0%
8 141
 
3.3%
9 154
 
3.6%
ValueCountFrequency (%)
9 154
 
3.6%
8 141
 
3.3%
7 217
 
5.0%
6 201
 
4.7%
5 185
 
4.3%
4 404
 
9.4%
3 466
 
10.8%
2 427
 
9.9%
1 1535
35.7%
0 570
 
13.3%

Over18
Boolean

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size166.8 KiB
True
4300 
ValueCountFrequency (%)
True 4300
100.0%
2023-12-05T01:37:15.181757image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

PercentSalaryHike
Real number (ℝ)

HIGH CORRELATION 

Distinct15
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.210698
Minimum11
Maximum25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size196.2 KiB
2023-12-05T01:37:15.386181image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile11
Q112
median14
Q318
95-th percentile22
Maximum25
Range14
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.6627771
Coefficient of variation (CV)0.2408027
Kurtosis-0.30670349
Mean15.210698
Median Absolute Deviation (MAD)2
Skewness0.82007788
Sum65406
Variance13.415936
MonotonicityNot monotonic
2023-12-05T01:37:15.801649image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
11 616
14.3%
13 616
14.3%
14 583
13.6%
12 577
13.4%
15 296
6.9%
18 260
6.0%
17 236
 
5.5%
16 230
 
5.3%
19 224
 
5.2%
22 167
 
3.9%
Other values (5) 495
11.5%
ValueCountFrequency (%)
11 616
14.3%
12 577
13.4%
13 616
14.3%
14 583
13.6%
15 296
6.9%
16 230
 
5.3%
17 236
 
5.5%
18 260
6.0%
19 224
 
5.2%
20 158
 
3.7%
ValueCountFrequency (%)
25 54
 
1.3%
24 59
 
1.4%
23 82
 
1.9%
22 167
3.9%
21 142
3.3%
20 158
3.7%
19 224
5.2%
18 260
6.0%
17 236
5.5%
16 230
5.3%

StandardHours
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size196.2 KiB
8
4300 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4300
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8
2nd row8
3rd row8
4th row8
5th row8

Common Values

ValueCountFrequency (%)
8 4300
100.0%

Length

2023-12-05T01:37:16.263453image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-05T01:37:16.715554image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
8 4300
100.0%

Most occurring characters

ValueCountFrequency (%)
8 4300
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4300
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 4300
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4300
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
8 4300
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4300
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8 4300
100.0%

StockOptionLevel
Categorical

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size196.2 KiB
0
1846 
1
1738 
2
466 
3
250 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4300
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row3
4th row3
5th row2

Common Values

ValueCountFrequency (%)
0 1846
42.9%
1 1738
40.4%
2 466
 
10.8%
3 250
 
5.8%

Length

2023-12-05T01:37:17.121203image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-05T01:37:17.597918image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 1846
42.9%
1 1738
40.4%
2 466
 
10.8%
3 250
 
5.8%

Most occurring characters

ValueCountFrequency (%)
0 1846
42.9%
1 1738
40.4%
2 466
 
10.8%
3 250
 
5.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4300
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1846
42.9%
1 1738
40.4%
2 466
 
10.8%
3 250
 
5.8%

Most occurring scripts

ValueCountFrequency (%)
Common 4300
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1846
42.9%
1 1738
40.4%
2 466
 
10.8%
3 250
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4300
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1846
42.9%
1 1738
40.4%
2 466
 
10.8%
3 250
 
5.8%

TotalWorkingYears
Real number (ℝ)

HIGH CORRELATION 

Distinct40
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.285116
Minimum0
Maximum40
Zeros31
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size196.2 KiB
2023-12-05T01:37:18.071145image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q16
median10
Q315
95-th percentile28
Maximum40
Range40
Interquartile range (IQR)9

Descriptive statistics

Standard deviation7.7900523
Coefficient of variation (CV)0.69029437
Kurtosis0.91470384
Mean11.285116
Median Absolute Deviation (MAD)4
Skewness1.1157752
Sum48526
Variance60.684915
MonotonicityNot monotonic
2023-12-05T01:37:18.583113image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
10 590
 
13.7%
6 368
 
8.6%
8 299
 
7.0%
9 278
 
6.5%
5 255
 
5.9%
1 239
 
5.6%
7 234
 
5.4%
4 185
 
4.3%
12 144
 
3.3%
3 123
 
2.9%
Other values (30) 1585
36.9%
ValueCountFrequency (%)
0 31
 
0.7%
1 239
5.6%
2 93
 
2.2%
3 123
 
2.9%
4 185
4.3%
5 255
5.9%
6 368
8.6%
7 234
5.4%
8 299
7.0%
9 278
6.5%
ValueCountFrequency (%)
40 6
 
0.1%
38 3
 
0.1%
37 12
0.3%
36 18
0.4%
35 9
 
0.2%
34 14
0.3%
33 21
0.5%
32 25
0.6%
31 27
0.6%
30 21
0.5%

TrainingTimesLastYear
Real number (ℝ)

ZEROS 

Distinct7
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.7962791
Minimum0
Maximum6
Zeros161
Zeros (%)3.7%
Negative0
Negative (%)0.0%
Memory size196.2 KiB
2023-12-05T01:37:19.010824image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q33
95-th percentile5
Maximum6
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.290142
Coefficient of variation (CV)0.46137811
Kurtosis0.49502929
Mean2.7962791
Median Absolute Deviation (MAD)1
Skewness0.54745355
Sum12024
Variance1.6644663
MonotonicityNot monotonic
2023-12-05T01:37:19.250067image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
2 1596
37.1%
3 1439
33.5%
4 359
 
8.3%
5 346
 
8.0%
1 209
 
4.9%
6 190
 
4.4%
0 161
 
3.7%
ValueCountFrequency (%)
0 161
 
3.7%
1 209
 
4.9%
2 1596
37.1%
3 1439
33.5%
4 359
 
8.3%
5 346
 
8.0%
6 190
 
4.4%
ValueCountFrequency (%)
6 190
 
4.4%
5 346
 
8.0%
4 359
 
8.3%
3 1439
33.5%
2 1596
37.1%
1 209
 
4.9%
0 161
 
3.7%

YearsAtCompany
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct37
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.0260465
Minimum0
Maximum40
Zeros126
Zeros (%)2.9%
Negative0
Negative (%)0.0%
Memory size196.2 KiB
2023-12-05T01:37:19.507467image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median5
Q39.25
95-th percentile20
Maximum40
Range40
Interquartile range (IQR)6.25

Descriptive statistics

Standard deviation6.1480358
Coefficient of variation (CV)0.87503489
Kurtosis3.9341023
Mean7.0260465
Median Absolute Deviation (MAD)3
Skewness1.7696152
Sum30212
Variance37.798344
MonotonicityNot monotonic
2023-12-05T01:37:19.795496image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
5 576
13.4%
1 499
11.6%
3 376
8.7%
2 369
8.6%
10 351
8.2%
4 324
 
7.5%
7 263
 
6.1%
8 235
 
5.5%
9 234
 
5.4%
6 223
 
5.2%
Other values (27) 850
19.8%
ValueCountFrequency (%)
0 126
 
2.9%
1 499
11.6%
2 369
8.6%
3 376
8.7%
4 324
7.5%
5 576
13.4%
6 223
 
5.2%
7 263
6.1%
8 235
5.5%
9 234
5.4%
ValueCountFrequency (%)
40 3
 
0.1%
37 3
 
0.1%
36 6
 
0.1%
34 3
 
0.1%
33 15
0.3%
32 9
0.2%
31 9
0.2%
30 3
 
0.1%
29 6
 
0.1%
27 6
 
0.1%

YearsSinceLastPromotion
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct16
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.19
Minimum0
Maximum15
Zeros1697
Zeros (%)39.5%
Negative0
Negative (%)0.0%
Memory size196.2 KiB
2023-12-05T01:37:20.063786image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q33
95-th percentile9
Maximum15
Range15
Interquartile range (IQR)3

Descriptive statistics

Standard deviation3.2308185
Coefficient of variation (CV)1.4752596
Kurtosis3.6234982
Mean2.19
Median Absolute Deviation (MAD)1
Skewness1.9896234
Sum9417
Variance10.438188
MonotonicityNot monotonic
2023-12-05T01:37:20.315671image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0 1697
39.5%
1 1050
24.4%
2 463
 
10.8%
7 219
 
5.1%
4 174
 
4.0%
3 155
 
3.6%
5 133
 
3.1%
6 93
 
2.2%
11 69
 
1.6%
8 54
 
1.3%
Other values (6) 193
 
4.5%
ValueCountFrequency (%)
0 1697
39.5%
1 1050
24.4%
2 463
 
10.8%
3 155
 
3.6%
4 174
 
4.0%
5 133
 
3.1%
6 93
 
2.2%
7 219
 
5.1%
8 54
 
1.3%
9 50
 
1.2%
ValueCountFrequency (%)
15 39
 
0.9%
14 27
 
0.6%
13 30
 
0.7%
12 30
 
0.7%
11 69
 
1.6%
10 17
 
0.4%
9 50
 
1.2%
8 54
 
1.3%
7 219
5.1%
6 93
2.2%

YearsWithCurrManager
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct18
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.1325581
Minimum0
Maximum17
Zeros760
Zeros (%)17.7%
Negative0
Negative (%)0.0%
Memory size196.2 KiB
2023-12-05T01:37:20.568445image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q37
95-th percentile11
Maximum17
Range17
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.5658313
Coefficient of variation (CV)0.86286295
Kurtosis0.16790039
Mean4.1325581
Median Absolute Deviation (MAD)3
Skewness0.8330911
Sum17770
Variance12.715153
MonotonicityNot monotonic
2023-12-05T01:37:20.794541image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
2 1009
23.5%
0 760
17.7%
7 631
14.7%
3 419
9.7%
8 312
 
7.3%
4 288
 
6.7%
1 222
 
5.2%
9 187
 
4.3%
5 93
 
2.2%
6 84
 
2.0%
Other values (8) 295
 
6.9%
ValueCountFrequency (%)
0 760
17.7%
1 222
 
5.2%
2 1009
23.5%
3 419
9.7%
4 288
 
6.7%
5 93
 
2.2%
6 84
 
2.0%
7 631
14.7%
8 312
 
7.3%
9 187
 
4.3%
ValueCountFrequency (%)
17 21
 
0.5%
16 5
 
0.1%
15 14
 
0.3%
14 15
 
0.3%
13 42
 
1.0%
12 54
 
1.3%
11 65
 
1.5%
10 79
 
1.8%
9 187
4.3%
8 312
7.3%
Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size196.2 KiB
3.0
1319 
4.0
1312 
2.0
839 
1.0
830 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters12900
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row3.0
3rd row2.0
4th row4.0
5th row4.0

Common Values

ValueCountFrequency (%)
3.0 1319
30.7%
4.0 1312
30.5%
2.0 839
19.5%
1.0 830
19.3%

Length

2023-12-05T01:37:21.044791image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-05T01:37:21.329792image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
3.0 1319
30.7%
4.0 1312
30.5%
2.0 839
19.5%
1.0 830
19.3%

Most occurring characters

ValueCountFrequency (%)
. 4300
33.3%
0 4300
33.3%
3 1319
 
10.2%
4 1312
 
10.2%
2 839
 
6.5%
1 830
 
6.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8600
66.7%
Other Punctuation 4300
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4300
50.0%
3 1319
 
15.3%
4 1312
 
15.3%
2 839
 
9.8%
1 830
 
9.7%
Other Punctuation
ValueCountFrequency (%)
. 4300
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 12900
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 4300
33.3%
0 4300
33.3%
3 1319
 
10.2%
4 1312
 
10.2%
2 839
 
6.5%
1 830
 
6.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12900
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 4300
33.3%
0 4300
33.3%
3 1319
 
10.2%
4 1312
 
10.2%
2 839
 
6.5%
1 830
 
6.4%

JobSatisfaction
Categorical

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size196.2 KiB
4.0
1334 
3.0
1296 
1.0
847 
2.0
823 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters12900
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4.0
2nd row2.0
3rd row2.0
4th row4.0
5th row1.0

Common Values

ValueCountFrequency (%)
4.0 1334
31.0%
3.0 1296
30.1%
1.0 847
19.7%
2.0 823
19.1%

Length

2023-12-05T01:37:21.566026image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-05T01:37:21.825822image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
4.0 1334
31.0%
3.0 1296
30.1%
1.0 847
19.7%
2.0 823
19.1%

Most occurring characters

ValueCountFrequency (%)
. 4300
33.3%
0 4300
33.3%
4 1334
 
10.3%
3 1296
 
10.0%
1 847
 
6.6%
2 823
 
6.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8600
66.7%
Other Punctuation 4300
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4300
50.0%
4 1334
 
15.5%
3 1296
 
15.1%
1 847
 
9.8%
2 823
 
9.6%
Other Punctuation
ValueCountFrequency (%)
. 4300
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 12900
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 4300
33.3%
0 4300
33.3%
4 1334
 
10.3%
3 1296
 
10.0%
1 847
 
6.6%
2 823
 
6.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12900
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 4300
33.3%
0 4300
33.3%
4 1334
 
10.3%
3 1296
 
10.0%
1 847
 
6.6%
2 823
 
6.4%

WorkLifeBalance
Categorical

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size196.2 KiB
3.0
2609 
2.0
1005 
4.0
450 
1.0
 
236

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters12900
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row4.0
3rd row1.0
4th row3.0
5th row3.0

Common Values

ValueCountFrequency (%)
3.0 2609
60.7%
2.0 1005
 
23.4%
4.0 450
 
10.5%
1.0 236
 
5.5%

Length

2023-12-05T01:37:22.060749image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-05T01:37:22.346341image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
3.0 2609
60.7%
2.0 1005
 
23.4%
4.0 450
 
10.5%
1.0 236
 
5.5%

Most occurring characters

ValueCountFrequency (%)
. 4300
33.3%
0 4300
33.3%
3 2609
20.2%
2 1005
 
7.8%
4 450
 
3.5%
1 236
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8600
66.7%
Other Punctuation 4300
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4300
50.0%
3 2609
30.3%
2 1005
 
11.7%
4 450
 
5.2%
1 236
 
2.7%
Other Punctuation
ValueCountFrequency (%)
. 4300
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 12900
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 4300
33.3%
0 4300
33.3%
3 2609
20.2%
2 1005
 
7.8%
4 450
 
3.5%
1 236
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12900
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 4300
33.3%
0 4300
33.3%
3 2609
20.2%
2 1005
 
7.8%
4 450
 
3.5%
1 236
 
1.8%

JobInvolvement
Categorical

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size196.2 KiB
3
2535 
2
1104 
4
420 
1
 
241

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4300
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row2
3rd row3
4th row2
5th row3

Common Values

ValueCountFrequency (%)
3 2535
59.0%
2 1104
25.7%
4 420
 
9.8%
1 241
 
5.6%

Length

2023-12-05T01:37:22.578033image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-05T01:37:22.844873image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
3 2535
59.0%
2 1104
25.7%
4 420
 
9.8%
1 241
 
5.6%

Most occurring characters

ValueCountFrequency (%)
3 2535
59.0%
2 1104
25.7%
4 420
 
9.8%
1 241
 
5.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4300
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 2535
59.0%
2 1104
25.7%
4 420
 
9.8%
1 241
 
5.6%

Most occurring scripts

ValueCountFrequency (%)
Common 4300
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 2535
59.0%
2 1104
25.7%
4 420
 
9.8%
1 241
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4300
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 2535
59.0%
2 1104
25.7%
4 420
 
9.8%
1 241
 
5.6%

PerformanceRating
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size196.2 KiB
3
3638 
4
662 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4300
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row4
3rd row3
4th row3
5th row3

Common Values

ValueCountFrequency (%)
3 3638
84.6%
4 662
 
15.4%

Length

2023-12-05T01:37:23.076861image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-05T01:37:23.344276image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
3 3638
84.6%
4 662
 
15.4%

Most occurring characters

ValueCountFrequency (%)
3 3638
84.6%
4 662
 
15.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4300
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 3638
84.6%
4 662
 
15.4%

Most occurring scripts

ValueCountFrequency (%)
Common 4300
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 3638
84.6%
4 662
 
15.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4300
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 3638
84.6%
4 662
 
15.4%

Interactions

2023-12-05T01:37:01.151737image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-05T01:36:11.496891image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-05T01:36:17.089805image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-05T01:36:23.990246image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-05T01:36:29.190641image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-05T01:36:37.101133image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-05T01:36:42.227855image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-05T01:36:48.575022image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-05T01:36:51.883975image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-05T01:36:54.682986image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-05T01:36:57.538199image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-05T01:37:01.552783image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-05T01:36:11.748451image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-05T01:36:17.850498image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-05T01:36:24.378786image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-05T01:36:29.792618image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-05T01:36:37.693651image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-05T01:36:42.731206image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-05T01:36:48.953137image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-05T01:36:52.136101image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-05T01:36:54.936700image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-05T01:36:57.772990image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-05T01:37:01.935505image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-05T01:36:11.984013image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-05T01:36:18.741947image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-05T01:36:24.834044image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-05T01:36:30.308138image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-05T01:36:38.109069image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-05T01:36:43.170203image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-05T01:36:49.310192image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-05T01:36:52.369326image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-05T01:36:55.187623image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-05T01:36:58.026790image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-05T01:37:02.337564image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-05T01:36:12.487968image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-05T01:36:19.462258image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-05T01:36:25.345366image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-05T01:36:30.852841image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-05T01:36:38.490711image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-05T01:36:44.004714image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-05T01:36:49.677954image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-05T01:36:52.613433image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-05T01:36:55.441219image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-05T01:36:58.295227image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-05T01:37:02.769932image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-05T01:36:13.005524image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-05T01:36:20.052601image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-05T01:36:25.761587image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-05T01:36:31.493230image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-05T01:36:38.933261image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-05T01:36:44.422540image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-05T01:36:50.052413image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-05T01:36:52.877737image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-05T01:36:55.706260image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-05T01:36:58.567925image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-05T01:37:03.172319image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-05T01:36:13.432630image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-05T01:36:20.846074image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-05T01:36:26.258305image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-05T01:36:32.596610image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-05T01:36:39.333670image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-05T01:36:44.840450image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-05T01:36:50.292803image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-05T01:36:53.127210image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-05T01:36:55.946584image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-05T01:36:58.799468image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-05T01:37:03.572228image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-05T01:36:13.893656image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-05T01:36:21.338087image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-05T01:36:26.700289image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-05T01:36:33.649702image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-05T01:36:39.729116image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-05T01:36:45.354002image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-05T01:36:50.552699image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-05T01:36:53.378332image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-05T01:36:56.212261image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-05T01:36:59.399904image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-05T01:37:03.933935image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-05T01:36:14.407874image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-05T01:36:21.854106image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-05T01:36:27.301977image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-05T01:36:34.788817image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-05T01:36:40.187771image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-05T01:36:45.646464image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-05T01:36:50.815331image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-05T01:36:53.647250image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-05T01:36:56.480180image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-05T01:36:59.669482image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-05T01:37:04.304431image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-05T01:36:14.855457image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-05T01:36:22.432500image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-05T01:36:27.796842image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-05T01:36:35.668753image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-05T01:36:40.649612image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-05T01:36:47.429141image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-05T01:36:51.092002image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-05T01:36:53.899330image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-05T01:36:56.739352image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-05T01:36:59.953315image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-05T01:37:04.627700image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-05T01:36:15.492117image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-05T01:36:22.861069image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-05T01:36:28.258339image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-05T01:36:36.136367image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-05T01:36:41.247839image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-05T01:36:47.788813image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-05T01:36:51.350725image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-05T01:36:54.161258image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-05T01:36:57.000050image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-05T01:37:00.315791image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-05T01:37:04.896907image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-05T01:36:16.205312image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-05T01:36:23.433404image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-05T01:36:28.714359image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-05T01:36:36.496773image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-05T01:36:41.782397image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-05T01:36:48.205064image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-05T01:36:51.619456image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-05T01:36:54.411590image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-05T01:36:57.273362image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-05T01:37:00.723961image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-12-05T01:37:23.588436image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
AgeAttritionBusinessTravelDepartmentDistanceFromHomeEducationEducationFieldEmployeeIDEnvironmentSatisfactionGenderJobInvolvementJobLevelJobRoleJobSatisfactionMaritalStatusMonthlyIncomeNumCompaniesWorkedPercentSalaryHikePerformanceRatingStockOptionLevelTotalWorkingYearsTrainingTimesLastYearWorkLifeBalanceYearsAtCompanyYearsSinceLastPromotionYearsWithCurrManager
Age1.0000.2200.0790.047-0.0080.0690.0580.0040.0650.0520.0550.0630.0780.0300.155-0.0270.358-0.0360.0580.0520.658-0.0500.0710.2540.1720.197
Attrition0.2201.0000.1220.072-0.0010.0190.092-0.0020.1210.0100.0340.0210.0580.1060.173-0.0260.0300.0330.0200.011-0.197-0.0350.099-0.191-0.050-0.175
BusinessTravel0.0790.1221.0000.0530.0160.0340.071-0.0030.0300.0320.0380.0590.0350.0340.045-0.0120.036-0.0270.0000.0240.024-0.0380.000-0.027-0.037-0.030
Department0.0470.0720.0531.0000.0250.0170.591-0.0020.0310.0000.0340.0350.0390.0140.047-0.040-0.016-0.0010.0180.038-0.0200.0140.0150.0030.0010.005
DistanceFromHome-0.008-0.0010.0160.0251.0000.0720.0520.0040.0690.0430.0650.0440.0550.0590.081-0.023-0.0380.0370.0670.080-0.0040.0110.0660.0180.0090.033
Education0.0690.0190.0340.0170.0721.0000.053-0.0110.0280.0340.0470.0410.0640.0300.005-0.002-0.013-0.0340.0720.0350.0150.0300.0300.0070.0200.021
EducationField0.0580.0920.0710.5910.0520.0531.000-0.0040.0520.0270.0350.0290.0390.0320.0540.0030.007-0.0050.0180.0370.025-0.0220.0340.0250.0480.020
EmployeeID0.004-0.002-0.003-0.0020.004-0.011-0.0041.0000.0000.0060.0000.0220.0000.0000.0000.008-0.000-0.0030.0290.0000.002-0.0100.0000.0010.0100.007
EnvironmentSatisfaction0.0650.1210.0300.0310.0690.0280.0520.0001.0000.0530.0190.0510.0350.0220.038-0.0040.006-0.0090.0300.033-0.0150.0180.0280.0100.025-0.003
Gender0.0520.0100.0320.0000.0430.0340.0270.0060.0531.0000.0150.0390.0330.0350.0230.013-0.073-0.0030.0510.014-0.045-0.0370.054-0.012-0.030-0.003
JobInvolvement0.0550.0340.0380.0340.0650.0470.0350.0000.0190.0151.0000.0370.0670.0320.0450.0180.021-0.0140.0000.000-0.004-0.0090.039-0.0020.0220.004
JobLevel0.0630.0210.0590.0350.0440.0410.0290.0220.0510.0390.0371.0000.0570.0320.0360.044-0.0090.0320.0330.048-0.029-0.0410.035-0.042-0.039-0.038
JobRole0.0780.0580.0350.0390.0550.0640.0390.0000.0350.0330.0670.0571.0000.0390.060-0.003-0.016-0.0110.0570.072-0.0200.0700.063-0.027-0.0410.006
JobSatisfaction0.0300.1060.0340.0140.0590.0300.0320.0000.0220.0350.0320.0320.0391.0000.017-0.003-0.0550.0300.0590.046-0.010-0.0220.0260.0150.007-0.015
MaritalStatus0.1550.1730.0450.0470.0810.0050.0540.0000.0380.0230.0450.0360.0600.0171.000-0.065-0.0530.0020.0000.021-0.0930.0130.033-0.068-0.013-0.045
MonthlyIncome-0.027-0.026-0.012-0.040-0.023-0.0020.0030.008-0.0040.0130.0180.044-0.003-0.003-0.0651.000-0.0410.0160.0630.054-0.0160.0080.0490.0300.0580.027
NumCompaniesWorked0.3580.0300.036-0.016-0.038-0.0130.007-0.0000.006-0.0730.021-0.009-0.016-0.055-0.053-0.0411.0000.0130.0650.0710.320-0.0100.082-0.167-0.064-0.140
PercentSalaryHike-0.0360.033-0.027-0.0010.037-0.034-0.005-0.003-0.009-0.003-0.0140.032-0.0110.0300.0020.0160.0131.0000.9990.065-0.037-0.0340.070-0.042-0.036-0.047
PerformanceRating0.0580.0200.0000.0180.0670.0720.0180.0290.0300.0510.0000.0330.0570.0590.0000.0630.0650.9991.0000.033-0.011-0.0240.0490.001-0.0130.003
StockOptionLevel0.0520.0110.0240.0380.0800.0350.0370.0000.0330.0140.0000.0480.0720.0460.0210.0540.0710.0650.0331.0000.012-0.0640.0400.0010.0050.001
TotalWorkingYears0.658-0.1970.024-0.020-0.0040.0150.0250.002-0.015-0.045-0.004-0.029-0.020-0.010-0.093-0.0160.320-0.037-0.0110.0121.000-0.0450.0640.5970.3350.498
TrainingTimesLastYear-0.050-0.035-0.0380.0140.0110.030-0.022-0.0100.018-0.037-0.009-0.0410.070-0.0220.0130.008-0.010-0.034-0.024-0.064-0.0451.0000.050-0.017-0.005-0.018
WorkLifeBalance0.0710.0990.0000.0150.0660.0300.0340.0000.0280.0540.0390.0350.0630.0260.0330.0490.0820.0700.0490.0400.0640.0501.0000.0070.001-0.003
YearsAtCompany0.254-0.191-0.0270.0030.0180.0070.0250.0010.010-0.012-0.002-0.042-0.0270.015-0.0680.030-0.167-0.0420.0010.0010.597-0.0170.0071.0000.5210.843
YearsSinceLastPromotion0.172-0.050-0.0370.0010.0090.0200.0480.0100.025-0.0300.022-0.039-0.0410.007-0.0130.058-0.064-0.036-0.0130.0050.335-0.0050.0010.5211.0000.467
YearsWithCurrManager0.197-0.175-0.0300.0050.0330.0210.0200.007-0.003-0.0030.004-0.0380.006-0.015-0.0450.027-0.140-0.0470.0030.0010.498-0.018-0.0030.8430.4671.000

Missing values

2023-12-05T01:37:05.338226image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-05T01:37:06.190577image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

EmployeeIDAgeAttritionBusinessTravelDepartmentDistanceFromHomeEducationEducationFieldEmployeeCountGenderJobLevelJobRoleMaritalStatusMonthlyIncomeNumCompaniesWorkedOver18PercentSalaryHikeStandardHoursStockOptionLevelTotalWorkingYearsTrainingTimesLastYearYearsAtCompanyYearsSinceLastPromotionYearsWithCurrManagerEnvironmentSatisfactionJobSatisfactionWorkLifeBalanceJobInvolvementPerformanceRating
0151NoTravel_RarelySales62Life Sciences1Female1Healthcare RepresentativeMarried131160.001.0Y11801.061003.04.02.033
1231YesTravel_FrequentlyResearch & Development101Life Sciences1Female1Research ScientistSingle41890.000.0Y23816.035143.02.04.024
2332NoTravel_FrequentlyResearch & Development174Other1Male4Sales ExecutiveMarried165616.251.0Y15835.025032.02.01.033
3438NoNon-TravelResearch & Development25Life Sciences1Male3Human ResourcesMarried83210.003.0Y118313.058754.04.03.023
4532NoTravel_RarelyResearch & Development101Medical1Male1Sales ExecutiveSingle23420.004.0Y12829.026044.01.03.033
5646NoTravel_RarelyResearch & Development83Life Sciences1Female4Research DirectorMarried40710.003.0Y138028.057773.02.02.033
6728YesTravel_RarelyResearch & Development112Medical1Male2Sales ExecutiveSingle58130.002.0Y20815.020001.03.01.034
7829NoTravel_RarelyResearch & Development183Life Sciences1Male2Sales ExecutiveMarried31430.002.0Y228310.020001.02.03.034
8931NoTravel_RarelyResearch & Development13Life Sciences1Male3Laboratory TechnicianMarried20440.000.0Y218010.029782.04.03.034
91025NoNon-TravelResearch & Development74Medical1Female4Laboratory TechnicianDivorced134640.001.0Y13816.026152.01.03.033
EmployeeIDAgeAttritionBusinessTravelDepartmentDistanceFromHomeEducationEducationFieldEmployeeCountGenderJobLevelJobRoleMaritalStatusMonthlyIncomeNumCompaniesWorkedOver18PercentSalaryHikeStandardHoursStockOptionLevelTotalWorkingYearsTrainingTimesLastYearYearsAtCompanyYearsSinceLastPromotionYearsWithCurrManagerEnvironmentSatisfactionJobSatisfactionWorkLifeBalanceJobInvolvementPerformanceRating
4399440034NoTravel_RarelyResearch & Development263Other1Female1Sales ExecutiveMarried165616.256.0Y138013.0311594.01.03.023
4400440137NoTravel_RarelyResearch & Development225Medical1Female2Manufacturing DirectorMarried30550.002.0Y148317.033023.01.02.033
4401440245NoTravel_FrequentlySales211Marketing1Male3Research ScientistMarried22890.004.0Y13809.033021.03.03.023
4402440337YesTravel_FrequentlySales23Marketing1Male1Laboratory TechnicianDivorced40010.006.0Y118117.021001.03.03.033
4403440439NoTravel_FrequentlyResearch & Development223Medical1Female1Manufacturing DirectorSingle129650.000.0Y198120.02191183.03.03.033
4404440529NoTravel_RarelySales43Other1Female2Human ResourcesSingle35390.001.0Y18806.026153.04.03.023
4405440642NoTravel_RarelyResearch & Development54Medical1Female1Research ScientistSingle60290.003.0Y178110.053024.01.03.033
4406440729NoTravel_RarelyResearch & Development24Medical1Male1Laboratory TechnicianDivorced26790.002.0Y158010.023024.04.03.023
4407440825NoTravel_RarelyResearch & Development252Life Sciences1Male2Sales ExecutiveMarried37020.000.0Y20805.044121.03.03.034
4408440942NoTravel_RarelySales182Medical1Male1Laboratory TechnicianDivorced23980.000.0Y148110.029784.01.03.023